





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy

@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """      ,snake_case__      ,)
class 		__a				(						snake_case__   ):
       __snake_case    : str   =		RobertaConfig
       __snake_case    : Dict   =		'''roberta'''



       def __init__(  self						: Dict       ,      UpperCAmelCase						: str      ):
           super().__init__(_UpperCAmelCase      )

           lowerCAmelCase_       :						Optional[Any]							= RobertaEmbeddings(_UpperCAmelCase      )
           self.init_weights()






@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """      ,snake_case__      ,)
class 		__a				(						snake_case__   ):
       __snake_case    : Optional[Any]   =		RobertaConfig
       __snake_case    : List[str]   =		'''roberta'''



       def __init__(  self						: Optional[int]       ,      UpperCAmelCase						: int      ):
           super().__init__(_UpperCAmelCase      )
           lowerCAmelCase_       :						Optional[int]							= config.num_labels
           lowerCAmelCase_       :						Dict							= config.num_hidden_layers

           lowerCAmelCase_       :						Optional[int]							= DeeRobertaModel(_UpperCAmelCase      )
           lowerCAmelCase_       :						int							= nn.Dropout(config.hidden_dropout_prob      )
           lowerCAmelCase_       :						Dict							= nn.Linear(config.hidden_size       ,      self.config.num_labels      )



       @add_start_docstrings_to_model_forward(_UpperCAmelCase      )
       def 			A				(  self						: Tuple       ,      UpperCAmelCase						: Union[str, Any]=None       ,      UpperCAmelCase						: List[str]=None       ,      UpperCAmelCase						: Any=None       ,      UpperCAmelCase						: List[Any]=None       ,      UpperCAmelCase						: str=None       ,      UpperCAmelCase						: int=None       ,      UpperCAmelCase						: Union[str, Any]=None       ,      UpperCAmelCase						: List[Any]=-1       ,      UpperCAmelCase						: Optional[Any]=False       ,      ):
           lowerCAmelCase_       :						Any							= self.num_layers
           try:
               lowerCAmelCase_       :						Union[str, Any]							= self.roberta(
                   _UpperCAmelCase       ,      attention_mask=_UpperCAmelCase       ,      token_type_ids=_UpperCAmelCase       ,      position_ids=_UpperCAmelCase       ,      head_mask=_UpperCAmelCase       ,      inputs_embeds=_UpperCAmelCase       ,      )

               lowerCAmelCase_       :						Dict							= outputs[1]

               lowerCAmelCase_       :						List[str]							= self.dropout(_UpperCAmelCase      )
               lowerCAmelCase_       :						Optional[Any]							= self.classifier(_UpperCAmelCase      )
               lowerCAmelCase_       :						Dict							= (logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
               lowerCAmelCase_       :						Union[str, Any]							= e.message
               lowerCAmelCase_       :						int							= e.exit_layer
               lowerCAmelCase_       :						Union[str, Any]							= outputs[0]

           if not self.training:
               lowerCAmelCase_       :						Union[str, Any]							= entropy(_UpperCAmelCase      )
               lowerCAmelCase_       :						Union[str, Any]							= []
               lowerCAmelCase_       :						str							= []
           if labels is not None:
               if self.num_labels == 1:
                   #  We are doing regression
                   lowerCAmelCase_       :						Dict							= MSELoss()
                   lowerCAmelCase_       :						Tuple							= loss_fct(logits.view(-1      )       ,      labels.view(-1      )      )
               else:
                   lowerCAmelCase_       :						Dict							= CrossEntropyLoss()
                   lowerCAmelCase_       :						Dict							= loss_fct(logits.view(-1       ,      self.num_labels      )       ,      labels.view(-1      )      )

               # work with highway exits
               lowerCAmelCase_       :						str							= []
               for highway_exit in outputs[-1]:
                   lowerCAmelCase_       :						str							= highway_exit[0]
                   if not self.training:
                       highway_logits_all.append(_UpperCAmelCase      )
                       highway_entropy.append(highway_exit[2]      )
                   if self.num_labels == 1:
                       #  We are doing regression
                       lowerCAmelCase_       :						Any							= MSELoss()
                       lowerCAmelCase_       :						List[Any]							= loss_fct(highway_logits.view(-1      )       ,      labels.view(-1      )      )
                   else:
                       lowerCAmelCase_       :						List[str]							= CrossEntropyLoss()
                       lowerCAmelCase_       :						Tuple							= loss_fct(highway_logits.view(-1       ,      self.num_labels      )       ,      labels.view(-1      )      )
                   highway_losses.append(_UpperCAmelCase      )

               if train_highway:
                   lowerCAmelCase_       :						str							= (sum(highway_losses[:-1]      ),) + outputs
                   # exclude the final highway, of course
               else:
                   lowerCAmelCase_       :						Tuple							= (loss,) + outputs
           if not self.training:
               lowerCAmelCase_       :						str							= outputs + ((original_entropy, highway_entropy), exit_layer)
               if output_layer >= 0:
                   lowerCAmelCase_       :						Union[str, Any]							= (
                       (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                   )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy







'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




